Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Детерминистический анализ сценариев× | Детерминистический анализ чувствительности× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1967 | 1950s–1970s (formalized) |
| Автор метода≠ | Kahn, H., Wiener, A. J. (RAND Corporation / Hudson Institute) | Saltelli, A. et al.; widely formalized across operations research and health economics |
| Тип≠ | Exploratory planning and decision-support framework | Parameter variation / robustness testing |
| Основополагающий источник≠ | Kahn, H., Wiener, A. J. (1967). The Year 2000: A Framework for Speculation on the Next Thirty-Three Years. Macmillan, New York. ISBN: 9780025604407 | Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons, Chichester. ISBN: 9780470870938 |
| Другие названия | DSA, Fixed-Input Scenario Analysis, Classical Scenario Analysis, Deterministic What-If Analysis | DSA, One-Way Sensitivity Analysis, Tornado Diagram Analysis, Parametric Sensitivity Analysis |
| Связанные≠ | 5 | 2 |
| Сводка≠ | Deterministic Scenario Analysis (DSA) is a structured planning method in which analysts construct a finite set of internally consistent future scenarios, each defined by fixed, precisely specified parameter values rather than probability distributions. By running a model or calculation under each scenario's fixed inputs, decision-makers can map how outcomes diverge across plausible futures and stress-test strategies without requiring full probabilistic characterization of uncertainty. | Deterministic Sensitivity Analysis (DSA) tests how model outputs change when individual or combined input parameters are varied across plausible ranges, one at a time or in structured combinations, without invoking probabilistic sampling. It is the standard approach in economic modeling, decision trees, and mathematical programming to identify which parameters drive conclusions and to demonstrate model robustness to regulators, reviewers, and stakeholders. |
| ScholarGateНабор данных ↗ |
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